Senior AI Systems Engineer — Agentic Platforms

Kindo•Los Angeles, CA

11h•$170,000 - $220,000•Hybrid

About The Position

Senior AI Systems Engineer — Agentic Platforms The Moment The role of the software engineer is changing. Autonomous agents can now execute real workflows, operate infrastructure, and improve over time. The hard problems are shifting from model demos to production systems: orchestration, memory, reliability, control, and security. OpenAI acquired OpenClaw. Meta paid $2B for Manus. The agent platform layer is becoming one of the most important layers in the stack. At Kindo, we’re already there. Our platform runs autonomous agents in production at real enterprises, automating DevOps and SecOps workflows with real permissions, real consequences, and real reliability requirements. About Kindo Kindo is an agent automation platform for DevOps and SecOps teams. We help organizations automate high-friction operational work using autonomous agents that run reliably, securely, and at scale. Our platform supports deployment on-prem, in hybrid environments, or in the cloud, with enterprise-grade security controls from day one. We’re a small, highly technical team with strong customer traction and real enterprise revenue. Engineers have direct ownership over critical systems and shape how the platform evolves. The Role You will design, build, and operate core systems that enable autonomous agents to function reliably in production. This is applied systems engineering with AI at the center, not ML research and not chatbot wrappers. You’ll build production-grade agentic workflows, retrieval and memory systems, multi-model execution, and tool-calling integrations that interact safely with enterprise systems. This is also frontier work. Many of the patterns for agentic systems are still emerging. You’ll explore new approaches, prototype quickly, and turn what works into durable production systems. At the same time, strong distributed systems fundamentals still apply. These systems must be reliable, secure, observable, debuggable, and maintainable under real-world conditions. How You Build AI is a first-class tool in how we engineer. You use AI across design, prototyping, implementation, testing, debugging, and incident response, and you continuously refine workflows that increase leverage without sacrificing quality. You pair that velocity with discipline: guardrails, verification, and architectural boundaries that keep systems safe as autonomy increases. What We’re Looking For We care far more about what you’ve built than what’s on your resume.

Requirements

Have built and operated complex backend or distributed systems in production
Have built LLM-powered or AI-native systems beyond demos, with real users and real constraints
Have strong judgment around reliability, security, observability, and failure modes
Are comfortable operating in ambiguous frontier areas and validating ideas through rapid iteration
Use AI as a core part of your development workflow, not as an occasional convenience
Operate with high ownership and autonomy and take systems end-to-end
TypeScript required, Python strongly preferred
Strong SQL proficiency
Experience with production infrastructure; Docker/Kubernetes experience is a plus

Nice To Haves

Familiarity with enterprise security patterns is a plus
Domain familiarity with DevOps, SecOps, or infrastructure automation is a plus

Responsibilities

Agent execution systems, including autonomous task loops, scheduling, triggers, and control planes
Retrieval and memory architectures, including context management, long-term memory, and structured memory
Multi-model routing and orchestration across providers, balancing quality, latency, cost, and failure modes
Tool-calling and integration frameworks for safe interaction with external services and enterprise environments
Reliability, security, and operability foundations, including evaluation, observability, failure isolation, and recovery paths